CN113505873A - Method for setting control parameters of double parallel inverters based on sailfish algorithm - Google Patents

Method for setting control parameters of double parallel inverters based on sailfish algorithm Download PDF

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CN113505873A
CN113505873A CN202110596715.8A CN202110596715A CN113505873A CN 113505873 A CN113505873 A CN 113505873A CN 202110596715 A CN202110596715 A CN 202110596715A CN 113505873 A CN113505873 A CN 113505873A
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聂晓华
苏才淇
王槐杰
高家明
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Abstract

The invention discloses a method for setting control parameters of a double parallel inverter based on a sailfish algorithm, which relates to the technical field of electric power and comprises the following steps: initializing to generate an initial population, adopting a sailfish algorithm and selecting an optimal adaptive value; designing two droop coefficients m and n of a control parameter of the double parallel inverters as setting parameters, and establishing a target function of the setting parameters; and respectively solving two droop coefficients m and n of the control parameters of the double parallel inverters according to the obtained optimal fitness value. The method introduces the flag fish algorithm (SFO) to carry out parameter setting on the double parallel inverter model, is very suitable for the optimization problem without structural modification due to the fact that the flag fish algorithm is a group-based algorithm, can play a great role in the problem of parameter setting, and has the advantages of fast global and local convergence and strong optimization capability. The optimal setting of the control parameters of the double parallel inverters is realized, and the method has great significance for improving the power quality.

Description

Method for setting control parameters of double parallel inverters based on sailfish algorithm
Technical Field
The invention relates to the technical field of electric power, in particular to a method for setting control parameters of a double parallel inverter based on a flag fish algorithm.
Background
The electric energy is used as a high-efficiency, quick and flexible clean energy source and has an irreplaceable position. The distributed power supply is connected with the power grid through the inverter, different control modes are adopted for the inverter, conversion of multiple working modes can be achieved, and flexibility of the power system is improved. The development of the inverter parallel technology increases the power supply capacity of the micro-grid and solves the problem of small power supply capacity of a single inverter. The waveform output by the inverter is greatly related to the control parameter, and the capacity and the redundancy of the system can be improved by connecting a plurality of inverters in parallel. The parallel inverter adopting the droop control mode can reduce communication and realize plug and play. The power sharing capability among inverters, the quick response capability when the load changes and the stability of the system are all related to the droop coefficient. Therefore, the selection of different droop coefficients according to the dynamic change of the load is the key of the high-efficiency parallel connection of the multiple inverters.
Disclosure of Invention
Aiming at the defects and problems in the prior art and aiming at obtaining the optimal droop coefficient parameter, the invention aims to provide a double parallel inverter control parameter setting method based on a flag fish algorithm.
The invention specifically adopts the following technical scheme:
a double parallel inverter control parameter setting method based on a flag fish algorithm comprises the following steps:
s1: initializing to generate an initial population, adopting a sailfish algorithm and selecting an optimal adaptive value;
s11: initializing, and randomly generating initial flag fish and sardine populations in a given search space, wherein the flag fish populations use XSFIndicates that sardine population is XFAnd expressing and respectively calculating the fitness values of all solutions of the flag fish and the sardine.
S12: calculating the fitness value of the flag fish and the sardine, recording the optimal fitness value and position, and selecting the population X with the best fitness value of the flag fisheliteSFShowing that the population with the best fitness value of sardines is selected by XinjuredSAnd (4) showing.
S13: the flag fish positions are updated, because the flag fish attacks not only from top to bottom or from right to left, but also from all sides, and the attack range is continuously reduced. Therefore, the flag fishes update the best solution of their position around a sphere, and the specific formula is as follows:
Figure BDA0003091411600000021
wherein λ isiThe coefficients are defined as follows:
λi＝2×rand(0,1)×PD-PD
wherein PD represents the density of prey population, and the specific formula is as follows:
Figure BDA0003091411600000022
wherein N isSFAnd NSRepresenting the number of flag fish and sardine, respectively.
S14: the sardine position is updated, at the beginning of hunting, the flag fish has more energy to catch the prey, the sardine is not tired or injured, and the sardine can keep a high escape speed. Gradually, the attacking ability of the flag fish is weakened along with the lapse of the hunting time, and the energy stored in the hunting objects is also reduced due to the fierce and frequent attacks, so that the ability to detect the position and direction information of the flag fish may be reduced, thereby affecting the escape strategy of the fish school. Finally, the sardine is hit by the beak of the flag fish, breaks free from the fish school and is captured quickly, and the specific formula for simulating the motion of the sardine is as follows:
Xi newS＝r×(Xi eliteSF-Xi oldS+AP)
where r is a random number between 0 and 1, representing the dispersion of the prey sardine around the predator, and AP represents the challenge power of the flag fish, defined as follows:
AP＝A×(1-(2×Itr×e))
s15: and (4) judging the attack force, wherein the position of the sardine is updated and is related to the attack force of the flag fish, and A and e in the formula control the change of the attack force to ensure that A is linearly changed to 0. The sardine total positions were updated with the above formula when AP > 0.5. When AP < 0.5. And updating the position of the sand-blasting part. The range of partial positions is defined as follows:
α＝NS×AP
β＝di×AP
where di is the number of variables for the ith iteration, α represents the number of sardines to be updated, and β represents the number of dimensions to be updated.
S16: the sardines and flag fishes are replaced, and in the final stage of hunting, injured sardines break loose from fish flocks and are captured quickly. In this algorithm, it is assumed that sardines are more suitable for predation than flag fish. In this case, the flag fish position is replaced with the latest position of the prey sardine, thereby increasing the chance of prey on new prey, as follows:
Xi SF＝Xi S,if f(Si)＜f(SFi)
s17: all fitness values are calculated, and the optimal fitness value and the position are updated and recorded.
S18: judging whether an iteration stop condition is met, if so, outputting an optimal solution, and ending the program; otherwise, steps S12 to S17 are repeated.
S2: designing two droop coefficients m and n of a control parameter of the double parallel inverters as setting parameters, and establishing a target function of the setting parameters;
according to the off-grid mode of the double parallel inverters based on droop control, active power and reactive power are adjusted, power sharing is achieved, and P in the double parallel inverters is subjected to power sharingsF sag factors m and QsF, setting a droop coefficient n, and correspondingly initializing 2 related parameters which are m and n respectively;
and calculating the fitness values of all fish schools in the population, and selecting the optimal fitness value in the population as the current position. In a double parallel inverter model, each iteration is the fitness value of the optimal position, namely each iteration has uniquely determined m and n parameter values, THD and ITAE are used as parameter setting indexes, different weight coefficients are added, multi-objective optimization is changed into a single-objective optimization problem, and the established objective function is as follows:
Figure BDA0003091411600000031
c1+d1+e+d＝1
in the formula: c. C1、d1E, d are different weight coefficients, c1＝d10.3, d 0.2, making multi-objective optimization become single-objective optimization problem, e1(t) is an active power error signal between the two inverters; e.g. of the type2(t) is a reactive power error signal between the two inverters; u. of1zo1And u1zon、u2zo1And u2zonThe fundamental wave and harmonic amplitude of the two inverter load terminal voltages are respectively.
S3: from the optimal fitness value obtained in step S1, two droop coefficients m and n of the double parallel inverter control parameter are obtained in combination with step S2.
The invention has the beneficial effects that:
the flag fish algorithm is a group-based algorithm, is very suitable for the optimization problem without structural modification, can play a great role in parameter setting problems, has the advantages of fast global and local convergence and strong optimization capability, can play a good role in setting droop control parameters of a double-inverter parallel model, can effectively adjust active power and reactive power, realizes power sharing, and improves the quality of output electric energy of inverters.
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FIG. 1 is a flow chart of a method for setting control parameters of a double parallel inverter based on a sailfish algorithm according to the present invention;
FIG. 2 is a topology diagram of a main circuit of a dual parallel inverter according to an embodiment of the present invention;
FIG. 3 is a graph of droop coefficient setting adaptation values in accordance with an embodiment of the present invention;
FIG. 4 is a load voltage graph according to an embodiment of the present invention;
fig. 5 is a load current graph according to an embodiment of the present invention.
Detailed Description
The technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiment of the present invention.
As shown in fig. 1-5, an embodiment of the present invention discloses a method for setting control parameters of a dual parallel inverter based on a swordfish algorithm, comprising the following steps:
step 1: initializing, and randomly generating initial flag fish and sardine populations in a given search space, wherein the flag fish populations use XSFIndicates that sardine population is XFAnd expressing and respectively calculating the fitness values of all solutions of the flag fish and the sardine.
Step 2: calculating the fitness value of the flag fish and the sardine, recording the optimal fitness value and position, and selecting the population X with the best fitness value of the flag fisheliteSFShowing that the population with the best fitness value of sardines is selected by XinjuredSAnd (4) showing.
Step 3: the flag fish positions are updated, because the flag fish attacks not only from top to bottom or from right to left, but also from all sides, and the attack range is continuously reduced. Therefore, the flag fishes update the best solution of their position around a sphere, and the specific formula is as follows:
Figure BDA0003091411600000041
where the λ i coefficients are defined as follows:
λi＝2×rand(0,1)×PD-PD
wherein PD represents the density of the prey population, and the specific formula is as follows:
Figure BDA0003091411600000042
wherein N isSFAnd NSRepresenting the number of flag fish and sardine, respectively.
Step 4: the sardine position is updated, at the beginning of hunting, the flag fish has more energy to catch the prey, the sardine is not tired or injured, and the sardine can keep a high escape speed. Gradually, the attacking ability of the flag fish is weakened along with the lapse of the hunting time, and the energy stored in the hunting objects is also reduced due to the fierce and frequent attacks, so that the ability to detect the position and direction information of the flag fish may be reduced, thereby affecting the escape strategy of the fish school. Finally, the sardine is hit by the beak of the flag fish, breaks free from the fish school and is captured quickly, and the specific formula for simulating the motion of the sardine is as follows:
Xi newS＝r×(Xi eliteSF-Xi oldS+AP)
where r is a random number between 0 and 1, representing the dispersion of the prey sardine around the predator, and AP represents the challenge power of the flag fish, defined as follows:
AP＝A×(1-(2×Itr×e))
step 5: and (4) judging the attack force, wherein the position of the sardine is updated and is related to the attack force of the flag fish, and A and e in the formula control the change of the attack force to ensure that A is linearly changed to 0. The sardine total positions were updated with the above formula when AP > 0.5. When AP < 0.5. And updating the position of the sand-blasting part. The range of partial positions is defined as follows:
α＝NS×AP
β＝di×AP
where di is the number of variables for the ith iteration, α represents the number of sardines to be updated, and β represents the number of dimensions to be updated.
Step 6: the sardines and flag fishes are replaced, and in the final stage of hunting, injured sardines break loose from fish flocks and are captured quickly. In this algorithm, it is assumed that sardines are more suitable for predation than flag fish. In this case, the flag fish position is replaced with the latest position of the prey sardine, thereby increasing the chance of prey on new prey, as follows:
Xi SF＝Xi S,if f(Si)＜f(SFi)
step 7: all fitness values are calculated, and the optimal fitness value and the position are updated and recorded.
Step 8: judging whether an iteration stop condition is met, if so, outputting an optimal solution, and ending the program; otherwise, steps 2 through 7 are repeated.
Step 9: and respectively solving two droop coefficients m and n of the control parameters of the double parallel inverters according to the obtained optimal result.
In this example, the initialization population in Step1 is to adjust active power and reactive power according to the off-grid mode of the double parallel inverters based on droop control, so as to achieve power sharing, and for P in the double parallel inverterssF sag factors m and QsF, setting the droop coefficient n, and initializing 2 corresponding parameters which are m and n respectively.
In this example, the fitness values of all fish schools in the population are calculated in Step2, and the best fitness value in the population is selected and set as the current position. In the double parallel inverter off-grid and grid-connected models, the fitness value of the optimal position is obtained in each iteration, namely, each iteration has uniquely determined m and n parameter values, THD and ITAE are used as parameter setting indexes, different weight coefficients are added, multi-objective optimization is changed into a single-objective optimization problem, and the established objective function is as follows:
Figure BDA0003091411600000061
c1+d1+e+d＝1
in the formula: c. C1d1ed is a different weight coefficient, c1＝d10.3, d 0.2, making multi-objective optimization become single-objective optimization problem, e1(t) is an active power error signal between the two inverters; e.g. of the type2(t) is a reactive power error signal between the two inverters; u. of1zo1And u1zon、u2zo1And u2zonThe fundamental wave and harmonic amplitude of the two inverter load terminal voltages are respectively.
In the present example, the maximum number of iterations is M-40, the population N-20, and the voltage outer loop in the voltage-current dual-loop controlPI regulating parameter KupAnd KuiTaking 10 and 100 respectively, the parameter K of the current inner loop P regulator is 5, PsF droop coefficients of m and QsF, setting intervals of droop coefficients n are all [0,1]。
By adopting the method for setting the control parameters of the double parallel inverters based on the sailfish algorithm, as can be seen from the graphs in fig. 3 to 5, the droop coefficient of the double parallel inverters is controlled to be rapidly converged in an off-grid mode or a grid-connected mode, the optimization precision is high, the method has good effect on the voltage waveform of the load with the double inverters connected in parallel, three-phase sinusoidal voltage waveforms can be effectively output, the voltage distortion rate is low, the active power and the reactive power can be adjusted by the method, and the power sharing is realized.
Finally, only specific embodiments of the present invention have been described in detail above. The invention is not limited to the specific embodiments described above. Equivalent modifications and substitutions by those skilled in the art are also within the scope of the present invention. Accordingly, equivalent alterations and modifications are intended to be included within the scope of the invention, without departing from the spirit and scope of the invention.

Claims (4)

1. A double parallel inverter control parameter setting method based on a flag fish algorithm is characterized by comprising the following steps:
s1: initializing to generate an initial population, adopting a sailfish algorithm and selecting an optimal adaptive value;
s2: designing two droop coefficients m and n of a control parameter of the double parallel inverters as setting parameters, and establishing a target function of the setting parameters;
s3: and respectively solving two droop coefficients m and n of the control parameters of the double parallel inverters according to the obtained optimal fitness value.
2. The method for setting the control parameter of the double parallel inverters based on the swordfish algorithm as claimed in claim 1, wherein the swordfish algorithm in step S1 specifically comprises:
s11, carrying out initializationRandomly generating an initial flag and sardine population within a given search space, wherein the flag population uses XSFIndicates that sardine population is XFRepresenting and respectively calculating the fitness values of all solutions of the flag fish and the sardine;
s12, calculating the fitness value of the flag fish and the sardine, recording the optimal fitness value and position, and selecting the population X with the best fitness value of the flag fisheliteSFShowing that the population with the best fitness value of sardines is selected by XinjuredSRepresents;
s13, flag fish position is updated, because flag fish do not attack from top to bottom or right to left, they can attack from all directions, and the attack range is continuously reduced. Therefore, the flag fishes update the best solution of their position around a sphere, and the specific formula is as follows:
Figure FDA0003091411590000011
wherein λ isiThe coefficients are defined as follows:
λi＝2×rand(0,1)×PD-PD
wherein PD represents the density of prey population, and the specific formula is as follows:
Figure FDA0003091411590000012
wherein N isSFAnd NSRespectively representing the number of the flag fishes and the sardines;
and S14, updating the position of the sardines, wherein the flag fishes have more energy to catch the prey at the beginning of hunting, the sardines cannot be tired or injured, and the sardines can keep high escape speed. Gradually, the attacking ability of the flag fish is weakened along with the lapse of the hunting time, the energy stored in the hunting object is reduced due to the fierce and frequent attacks, the ability of detecting the position and direction information of the flag fish is possibly reduced, thereby influencing the escape strategy of the fish school, finally, the sardine is hit by the beak of the flag fish, breaks free from the fish school and is captured quickly, and the concrete formula of simulating the sardine movement is as follows:
Xi newS＝r×(Xi eliteSF-Xi oldS+AP)
where r is a random number between 0 and 1, representing the dispersion of the prey sardine around the predator, and AP represents the challenge power of the flag fish, defined as follows:
AP＝A×(1-(2×Itr×e))
s15, attack force judgment, wherein the updating of the position of the sardine is related to the attack force of the flag fish, and A and e in the formula control the change of the attack force to ensure that A is linearly changed to 0; updating the total position of the sardine with the above formula when the AP is greater than 0.5; updating the salbutan part location when AP < 0.5; the range of partial positions is defined as follows:
α＝NS×AP
β＝di×AP
wherein d isiThe number of variables of the ith iteration is, alpha represents the number of sardines to be updated, and beta represents the number of dimensions to be updated;
s16, replacing the sardines and the flag fishes, wherein the injured sardines break loose from fish schools and are captured quickly in the final stage of hunting; in this algorithm, it is assumed that sardines are more suitable for predation than flag fish. In this case, the flag fish position is replaced with the latest position of the prey sardine, thereby increasing the chance of prey on new prey, as follows:
Xi SF＝Xi S,if f(Si)＜f(SFi)
s17: calculating all fitness values, and updating and recording the optimal fitness value and position;
s18: judging whether an iteration stop condition is met, if so, outputting an optimal solution, and ending the program; otherwise, steps S12 to S17 are repeated.
3. The method for setting the control parameters of the double parallel inverters based on the sailfish algorithm as claimed in claim 2, wherein the method is characterized in thatIn the following steps: in the step S2, active power and reactive power are adjusted according to the droop control-based off-grid mode of the double parallel inverters, so as to achieve power sharing, and P in the double parallel inverterssF sag factors m and QsF, setting the droop coefficient n, and initializing 2 corresponding parameters which are m and n respectively.
4. The method for setting the control parameter of the double parallel inverters based on the sailfish algorithm according to claim 3, characterized in that: in the step S1, fitness values of all fish schools in the population are calculated, and an optimal fitness value in the population is selected to be set as a current position, in the dual parallel inverter model in the step S2, the fitness value of the optimal position is obtained in each iteration, that is, each iteration has uniquely determined m and n parameter values, THD and ITAE are used as parameter setting indexes, different weight coefficients are added, and multi-objective optimization is changed into a single-objective optimization problem, so that an established objective function is as follows:
Figure FDA0003091411590000031
c1+d1+e+d＝1
in the formula: c. C1、d1E, d are different weight coefficients, c1＝d10.3, d 0.2, making multi-objective optimization become single-objective optimization problem, e1(t) is an active power error signal between the two inverters; e.g. of the type2(t) is a reactive power error signal between the two inverters; u. of1zo1And u1zon、u2zo1And u2zonThe fundamental wave and harmonic amplitude of the two inverter load terminal voltages are respectively.
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